My name is Adam Mallen, and I'm a graduate student in the Computational Sciences PhD program at Marquette University. This page serves as a resource for me while working on my research.
Nonlinear Filtering for Data Assimilation of Semi-Lagrangian Ocean Glider Data
The goal of this project is to develop efficient techniques for assimilating glider data into nonlinear ocean models. First, it will involve taking current particle filtering data assimilation techniques for 2D ocean models and scaling them to work on higher dimensional, more realistic, and more complex models—such as models with unknown time varying strengths of vortices—in a computationally feasible way. To start, these techniques will focus on assimilating data from tracers which only move with the flow, unlike gliders which can maneuver themselves. Then, the next task is to develop techniques for assimilating glider-like data (instead of tracer data) into these models and utilizing model-based prediction to help inform future flight paths of the glider. The final task will be to scale the ocean model to include a third spatial dimension and adapting the previous method to inform the glider's future flight path in three dimensions.
The link above contains an outline/timeline for my research. Each major revision is described and dated.
TODO: Add papers! Each paper should link to its own page. Those pages should each contain a short description (written by me) and a link to the real paper.